Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075

Detalhes bibliográficos
Autor(a) principal: Pereira, Robson Bruno Dutra
Data de Publicação: 2018
Outros Autores: Leite, Rodrigo Reis, Alvim, Aline Cunha, Paiva, Anderson Paulo de, Balestrassi, Pedro Paulo, Ferreira, João Roberto, Davim, J. Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/28269
Resumo: Helical milling is an advanced hole-making process and different approaches considering controllable variables have been presented addressing modelling and optimization of machining forces in helical milling. None of them considers the importance of the noise variables and the fact that machining forces components are usually correlated. Exploring this issue, this paper presents a multivariate robust modelling and optimization of cutting forces of the helical milling of the aluminum alloy Al 7075. For the study, the tool overhang length was defined as noise variable since in cavities machining there are specific workpiece geometries that constrain this variable; the controllable variables were axial feed per tooth, tangential feed per tooth and cutting speed. The cutting forces in the workpiece coordinate system were measured and the components in the tool coordinate system, i.e., the axial and radial forces, were evaluated. Since these two outcomes are correlated, the weighted principal component analysis was performed together with the robust parameter design to allow the multivariate robust modelling of the mean and variance equations. The normal boundary intersection method was used to obtain a set of Pareto robust optimal solutions related to the mean and variance equations of the weighted principal component. The optimization of the weighted principal component through the normal boundary intersection method was performed and the results evaluated in the axial and radial cutting forces components. Confirmation runs were carried out and it was possible to conclude that the models presented good fit with experimental data and that the Pareto optimal point chosen for performing the confirmation runs is robust to the tool overhang length variation. Finally, the cutting force models were also presented for mean and variance in the workpiece coordinate system in the time domain, presenting low error regarding the experimental test, endorsing the results.
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spelling Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075Helical millingCutting forcesRobust parameter designMultivariate mean square errorWeighted principal componentNormal boundary intersectionHelical milling is an advanced hole-making process and different approaches considering controllable variables have been presented addressing modelling and optimization of machining forces in helical milling. None of them considers the importance of the noise variables and the fact that machining forces components are usually correlated. Exploring this issue, this paper presents a multivariate robust modelling and optimization of cutting forces of the helical milling of the aluminum alloy Al 7075. For the study, the tool overhang length was defined as noise variable since in cavities machining there are specific workpiece geometries that constrain this variable; the controllable variables were axial feed per tooth, tangential feed per tooth and cutting speed. The cutting forces in the workpiece coordinate system were measured and the components in the tool coordinate system, i.e., the axial and radial forces, were evaluated. Since these two outcomes are correlated, the weighted principal component analysis was performed together with the robust parameter design to allow the multivariate robust modelling of the mean and variance equations. The normal boundary intersection method was used to obtain a set of Pareto robust optimal solutions related to the mean and variance equations of the weighted principal component. The optimization of the weighted principal component through the normal boundary intersection method was performed and the results evaluated in the axial and radial cutting forces components. Confirmation runs were carried out and it was possible to conclude that the models presented good fit with experimental data and that the Pareto optimal point chosen for performing the confirmation runs is robust to the tool overhang length variation. Finally, the cutting force models were also presented for mean and variance in the workpiece coordinate system in the time domain, presenting low error regarding the experimental test, endorsing the results.Springer Verlag2020-04-21T11:59:59Z2018-03-01T00:00:00Z2018-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/28269eng0268-376810.1007/s00170-017-1398-3Pereira, Robson Bruno DutraLeite, Rodrigo ReisAlvim, Aline CunhaPaiva, Anderson Paulo deBalestrassi, Pedro PauloFerreira, João RobertoDavim, J. Pauloinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:54:40Zoai:ria.ua.pt:10773/28269Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:00:50.982010Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
title Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
spellingShingle Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
Pereira, Robson Bruno Dutra
Helical milling
Cutting forces
Robust parameter design
Multivariate mean square error
Weighted principal component
Normal boundary intersection
title_short Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
title_full Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
title_fullStr Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
title_full_unstemmed Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
title_sort Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075
author Pereira, Robson Bruno Dutra
author_facet Pereira, Robson Bruno Dutra
Leite, Rodrigo Reis
Alvim, Aline Cunha
Paiva, Anderson Paulo de
Balestrassi, Pedro Paulo
Ferreira, João Roberto
Davim, J. Paulo
author_role author
author2 Leite, Rodrigo Reis
Alvim, Aline Cunha
Paiva, Anderson Paulo de
Balestrassi, Pedro Paulo
Ferreira, João Roberto
Davim, J. Paulo
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Pereira, Robson Bruno Dutra
Leite, Rodrigo Reis
Alvim, Aline Cunha
Paiva, Anderson Paulo de
Balestrassi, Pedro Paulo
Ferreira, João Roberto
Davim, J. Paulo
dc.subject.por.fl_str_mv Helical milling
Cutting forces
Robust parameter design
Multivariate mean square error
Weighted principal component
Normal boundary intersection
topic Helical milling
Cutting forces
Robust parameter design
Multivariate mean square error
Weighted principal component
Normal boundary intersection
description Helical milling is an advanced hole-making process and different approaches considering controllable variables have been presented addressing modelling and optimization of machining forces in helical milling. None of them considers the importance of the noise variables and the fact that machining forces components are usually correlated. Exploring this issue, this paper presents a multivariate robust modelling and optimization of cutting forces of the helical milling of the aluminum alloy Al 7075. For the study, the tool overhang length was defined as noise variable since in cavities machining there are specific workpiece geometries that constrain this variable; the controllable variables were axial feed per tooth, tangential feed per tooth and cutting speed. The cutting forces in the workpiece coordinate system were measured and the components in the tool coordinate system, i.e., the axial and radial forces, were evaluated. Since these two outcomes are correlated, the weighted principal component analysis was performed together with the robust parameter design to allow the multivariate robust modelling of the mean and variance equations. The normal boundary intersection method was used to obtain a set of Pareto robust optimal solutions related to the mean and variance equations of the weighted principal component. The optimization of the weighted principal component through the normal boundary intersection method was performed and the results evaluated in the axial and radial cutting forces components. Confirmation runs were carried out and it was possible to conclude that the models presented good fit with experimental data and that the Pareto optimal point chosen for performing the confirmation runs is robust to the tool overhang length variation. Finally, the cutting force models were also presented for mean and variance in the workpiece coordinate system in the time domain, presenting low error regarding the experimental test, endorsing the results.
publishDate 2018
dc.date.none.fl_str_mv 2018-03-01T00:00:00Z
2018-03
2020-04-21T11:59:59Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/28269
url http://hdl.handle.net/10773/28269
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0268-3768
10.1007/s00170-017-1398-3
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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